def test_array_of_dt64_nat_raises(self):
# GH#39462
nat = np.datetime64("NaT", "ns")
arr = np.array([nat], dtype=object)
# TODO: should be TypeError?
msg = "Invalid type for timedelta scalar"
with pytest.raises(ValueError, match=msg):
TimedeltaIndex(arr)
with pytest.raises(ValueError, match=msg):
TimedeltaArray._from_sequence(arr)
with pytest.raises(ValueError, match=msg):
sequence_to_td64ns(arr)
def __new__(cls, data=None, unit=None, freq=None, start=None, end=None,
periods=None, closed=None, dtype=_TD_DTYPE, copy=False,
name=None, verify_integrity=None):
if verify_integrity is not None:
warnings.warn("The 'verify_integrity' argument is deprecated, "
"will be removed in a future version.",
FutureWarning, stacklevel=2)
else:
verify_integrity = True
if data is None:
freq, freq_infer = dtl.maybe_infer_freq(freq)
warnings.warn("Creating a TimedeltaIndex by passing range "
"endpoints is deprecated. Use "
"`pandas.timedelta_range` instead.",
FutureWarning, stacklevel=2)
result = TimedeltaArray._generate_range(start, end, periods, freq,
closed=closed)
return cls._simple_new(result._data, freq=freq, name=name)
if is_scalar(data):
raise TypeError('{cls}() must be called with a '
'collection of some kind, {data} was passed'
.format(cls=cls.__name__, data=repr(data)))
if unit in {'Y', 'y', 'M'}:
warnings.warn("M and Y units are deprecated and "
"will be removed in a future version.",
FutureWarning, stacklevel=2)
if isinstance(data, TimedeltaArray):
if copy:
data = data.copy()
return cls._simple_new(data, name=name, freq=freq)
if (isinstance(data, TimedeltaIndex) and
freq is None and name is None):
if copy:
return data.copy()
else:
return data._shallow_copy()
# - Cases checked above all return/raise before reaching here - #
tdarr = TimedeltaArray._from_sequence(data, freq=freq, unit=unit,
dtype=dtype, copy=copy)
return cls._simple_new(tdarr._data, freq=tdarr.freq, name=name)
def __new__(cls, data=None, unit=None, freq=None, start=None, end=None,
periods=None, closed=None, dtype=_TD_DTYPE, copy=False,
name=None, verify_integrity=None):
if verify_integrity is not None:
warnings.warn("The 'verify_integrity' argument is deprecated, "
"will be removed in a future version.",
FutureWarning, stacklevel=2)
else:
verify_integrity = True
if data is None:
freq, freq_infer = dtl.maybe_infer_freq(freq)
warnings.warn("Creating a TimedeltaIndex by passing range "
"endpoints is deprecated. Use "
"`pandas.timedelta_range` instead.",
FutureWarning, stacklevel=2)
result = TimedeltaArray._generate_range(start, end, periods, freq,
closed=closed)
return cls._simple_new(result._data, freq=freq, name=name)
if is_scalar(data):
raise TypeError('{cls}() must be called with a '
'collection of some kind, {data} was passed'
.format(cls=cls.__name__, data=repr(data)))
if unit in {'Y', 'y', 'M'}:
warnings.warn("M and Y units are deprecated and "
"will be removed in a future version.",
FutureWarning, stacklevel=2)
if isinstance(data, TimedeltaArray):
if copy:
data = data.copy()
return cls._simple_new(data, name=name, freq=freq)
if (isinstance(data, TimedeltaIndex) and
freq is None and name is None):
if copy:
return data.copy()
else:
return data._shallow_copy()
# - Cases checked above all return/raise before reaching here - #
tdarr = TimedeltaArray._from_sequence(data, freq=freq, unit=unit,
dtype=dtype, copy=copy)
return cls._simple_new(tdarr._data, freq=tdarr.freq, name=name)
def __new__(
cls,
data=None,
unit=None,
freq=lib.no_default,
closed=None,
dtype=TD64NS_DTYPE,
copy=False,
name=None,
):
name = maybe_extract_name(name, data, cls)
if is_scalar(data):
raise TypeError(
f"{cls.__name__}() must be called with a "
f"collection of some kind, {repr(data)} was passed")
if unit in {"Y", "y", "M"}:
raise ValueError(
"Units 'M' and 'Y' are no longer supported, as they do not "
"represent unambiguous timedelta values durations.")
if isinstance(data, TimedeltaArray) and freq is lib.no_default:
if copy:
data = data.copy()
return cls._simple_new(data, name=name)
if isinstance(
data,
TimedeltaIndex) and freq is lib.no_default and name is None:
if copy:
return data.copy()
else:
return data._shallow_copy()
# - Cases checked above all return/raise before reaching here - #
tdarr = TimedeltaArray._from_sequence(data,
freq=freq,
unit=unit,
dtype=dtype,
copy=copy)
return cls._simple_new(tdarr, name=name)
def __new__(
cls,
data=None,
unit=None,
freq=None,
closed=None,
dtype=_TD_DTYPE,
copy=False,
name=None,
):
if is_scalar(data):
raise TypeError(
"{cls}() must be called with a "
"collection of some kind, {data} was passed".format(
cls=cls.__name__, data=repr(data)
)
)
if unit in {"Y", "y", "M"}:
raise ValueError(
"Units 'M' and 'Y' are no longer supported, as they do not "
"represent unambiguous timedelta values durations."
)
if isinstance(data, TimedeltaArray):
if copy:
data = data.copy()
return cls._simple_new(data, name=name, freq=freq)
if isinstance(data, TimedeltaIndex) and freq is None and name is None:
if copy:
return data.copy()
else:
return data._shallow_copy()
# - Cases checked above all return/raise before reaching here - #
tdarr = TimedeltaArray._from_sequence(
data, freq=freq, unit=unit, dtype=dtype, copy=copy
)
return cls._simple_new(tdarr._data, freq=tdarr.freq, name=name)
